
 [1]}
Let \(M\)  be an \(n_1 \times n_2\)  sized matrix of rank \(r\) .
Then, the nuclear norm minimization suggested in (1) would recover the underlying matrix using \(\Vert  \Omega \Vert  > 32 \mathrm {max}(\mu _0^2, \mu _1)\)  entries with probability \(1 - 6 \log {n_2}(n_1+n_2)^{2-2\beta } - n^{2-2\beta ^{1/2} }_2\)  whenever the following two conditions got satisfied:
